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Authors: Yaguang Sun and Bernhard Bauer

Affiliation: University of Augsburg, Germany

ISBN: 978-989-758-187-8

Keyword(s): Business Process Model Abstraction, Business Process Mining, Workflow Discovery, Graph Clustering, Trace Clustering.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Management ; Ontologies and the Semantic Web ; Sensor Networks ; Signal Processing ; Society, e-Business and e-Government ; Soft Computing ; Web Information Systems and Technologies

Abstract: Process model discovery is a significant research topic in the business process mining area. However, existing workflow discovery techniques run into a stone wall while dealing with event logs generated from highly flexible environments because the raw models mined from such logs often suffer from the problem of inaccuracy and high complexity. In this paper, we propose a new process model abstraction technique for solving this problem. The proposed technique is able to optimise the quality of the potential high level model (abstraction model) so that a high-quality abstraction model can be acquired and also considers the quality of the submodels generated where each sub-model is employed to show the details of its relevant high level activity in the high level model.

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Paper citation in several formats:
Sun, Y. and Bauer, B. (2016). A Graph and Trace Clustering-based Approach for Abstracting Mined Business Process Models.In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-187-8, pages 63-74. DOI: 10.5220/0005833900630074

@conference{iceis16,
author={Yaguang Sun. and Bernhard Bauer.},
title={A Graph and Trace Clustering-based Approach for Abstracting Mined Business Process Models},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2016},
pages={63-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005833900630074},
isbn={978-989-758-187-8},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Graph and Trace Clustering-based Approach for Abstracting Mined Business Process Models
SN - 978-989-758-187-8
AU - Sun, Y.
AU - Bauer, B.
PY - 2016
SP - 63
EP - 74
DO - 10.5220/0005833900630074

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